1. Field of the Invention
The present invention relates generally to channel estimators for use in radio communication devices, such as radiotelephones and, more particularly, relates to a method and apparatus for initializing a channel estimator. In a preferred embodiment described herein, the channel estimator is a predictive least mean squares channel estimator.
2. Description of the Related Art
Adaptive channel estimators track the channel impulse response, represented by H(n), using received channel samples and symbols decoded by a detector such as an Ungerboeck Maximum Likelihood Sequence Estimator (MLSE). In Time Division Multiplex (TDM) systems such as the North American Digital Cellular (NADC) system, there is a synchronous codeword at the beginning of each frame of data. Typically the synchronous codeword is used to initialize an adaptive channel estimator. Two conventional approaches used to initialize the adaptive channel estimator are channel sounding, described with reference to FIG. 1, and training using the synchronous codeword, described with reference to FIG. 2.
FIG. 1 illustrates a block diagram of an equalizer 10 and a corresponding data stream 11 using channel sounding followed by training to perform channel initialization, in accordance with the prior art. The equalizer 10 generally includes a channel sounding block 12, a matched filter 13, a channel estimator 14 and a maximum likelihood sequence estimator (MLSE) 15. The data stream 11 represents the samples received by the equalizer 10 and generally includes a synchronous codeword 16 followed by data 17, as is well known in the art. The operation of the equalizer 10 responsive to receiving the data stream 11 is well known in the art. The channel sounding approach requires multiple complex correlations of received in-phase (I) and quadrature-phase (Q) samples with the synchronous codeword to produce an initial channel estimate H (0), as is well known in the art.
FIG. 2 illustrates a block diagram of an equalizer 20 and a corresponding data stream 21 using training to perform channel initialization, in accordance with the prior art. The equalizer 20 generally includes a matched filter 22, a channel estimator 23 and a maximum likelihood sequence estimator (MLSE) 24. The data stream 21 represents the samples received by the equalizer 20 and generally includes a synchronous codeword 25 followed by data 26, as is well known in the art. The operation of the equalizer 20 responsive to receiving the data stream 21 is well known in the art. To perform channel initialization via the training approach, as illustrated in FIG. 2, the channel pulse response, Ĥ (xe2x88x9214), is set to an arbitrary constant (e.g. the all-zero vector) and the channel estimator is operated using the known symbols of the synchronous codeword 25. The goal is to have the channel estimator 23 converge to the actual channel response by the time data 26 is input to the channel estimator 23 at time n=0.
Each of these approaches has its drawbacks, especially when the channel estimator 14 in FIG. 1 or the channel estimator 23 in FIG. 2 is a predictive Least Mean Squares (LMS) adaptive filter. The LMS adaptive filter has essentially two estimators: one estimator for the channel response (i.e. the LMS estimator), and one estimator for the rate of change of the channel response (i.e. the predictor estimator). Each of these estimators must be initialized at the beginning of each frame.
In light of these two conventional approaches, conventional channel sounding alone is a sub-optimal technique of initialization because it initializes the LMS estimator but not the predictor estimator. Training alone, as described with FIG. 2, is a sub-optimal technique because training the LMS estimator (from a constant zero) tends to incorrectly train the predictor estimator, and there are not enough symbols in the synchronous codeword to compensate for this with conventional training. Further, channel sounding followed by training, as described with FIG. 1, helps somewhat, but this solution requires excessive hardware and current drain. For example, even if channel sounding followed by training was accomplished with a significant amount of hardware reuse, channel sounding would still require about 25,000 gates. Accordingly, there is a need for a method for initializing a predictive least mean squares channel estimator that solves the problem of initializing the LMS estimator and the predictor estimator to improve performance while minimizing hardware and current drain.